import numpy as np
import platgo as pg
import scipy.io as sio


class CEC_2020_F2(pg.Problem):

    def __init__(self, D=None) -> None:
        self.name = 'CEC_2020_F2'
        self.type['single'], self.type['real'] = [True] * 2
        self.M = 1
        load_path = 'CEC2020.mat'
        load_data = sio.loadmat(load_path)
        mat = []
        for k in load_data.items():
            mat.append(k)
        self.D = D
        self.O = mat[3][1][0][1][0][0][0]
        if self.D is None or self.D < 10:
            self.D = 5
            self.Mat = mat[3][1][0][1][0][0][1]
        elif self.D < 15:
            self.D = 10
            self.Mat = mat[3][1][0][1][0][0][2]
        elif self.D < 20:
            self.D = 15
            self.Mat = mat[3][1][0][1][0][0][3]
        else:
            self.D = 20
            self.Mat = mat[3][1][0][1][0][0][4]
        lb = [-100] * self.D
        ub = [100] * self.D
        self.borders = np.array([lb, ub])
        super().__init__()

    def cal_obj(self, pop: pg.Population) -> None:
        Z = pop.decs - np.tile(self.O[0][0: pop.decs.shape[1]], (pop.decs.shape[0], 1))
        Y = 10 * np.dot(Z, self.Mat.T)
        Z = Y + (4.2097e+2)
        g = Z * np.sin(np.sqrt(np.abs(Z)))
        temp = 500 - np.mod(Z[Z > 500], 500)
        g[Z > 500] = temp * np.sin(np.sqrt(np.abs(temp))) - (Z[Z > 500] - 500) ** 2 / 10000 / self.D
        temp = np.mod(np.abs(Z[Z < -500]), 500) - 500
        g[Z < -500] = temp * np.sin(np.sqrt(np.abs(temp))) - (Z[Z < -500] - 500) / 10000 / self.D
        pop.objv = 1100 + 418.9829 * self.D - np.sum(g, axis=1)
        pop.objv = pop.objv.reshape(pop.objv.shape[0], 1)

    def get_optimal(self) -> np.ndarray:
        pass


if __name__ == '__main__':
    problem = CEC_2020_F2()
    alg = pg.algorithms.GA(problem=problem, maxgen=100)
    pop = alg.go(100)
    print(pop)
